scholarly journals Choosing the right COVID-19 indicator: crude mortality, case fatality, and infection fatality rates influence policy preferences, behaviour, and understanding

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Chiara Natalie Focacci ◽  
Pak Hung Lam ◽  
Yu Bai
Keyword(s):  
Mortality Rate ◽  
Case Fatality ◽  
Policy Preferences ◽  
Fatality Rate ◽  
Change Policy ◽  
Crude Mortality Rate ◽  
Crude Mortality ◽  
Mortality Case ◽  
Types Of Information ◽  
The Right

AbstractIndividuals worldwide are overwhelmed with news about COVID-19. In times of pandemic, media alternate the usage of different COVID-19 indicators, ranging from the more typical crude mortality rate to the case fatality rate, and the infection fatality rate continuously. In this article, we used experimental methods to test whether and how the treatment of individuals with different types of information on COVID-19 is able to change policy preferences, individual and social behaviours, and the understanding of COVID-19 indicators. Results show that while the usage of the crude mortality rate proves to be more efficient in terms of supporting policy preferences and behaviours to contain the virus, all indicators suffer from a significant misunderstanding on behalf of the population.

scholarly journals The Relationship Between Demographic, Socioeconomic, and Health-Related Parameters and the Impact of COVID-19 on 24 Regions in India: Exploratory Cross-Sectional Study (Preprint)

2020 ◽  
Author(s):  
Ravi Philip Rajkumar
Keyword(s):  
Mortality Rate ◽  
Sex Ratio ◽  
Multivariate Analyses ◽  
Case Fatality ◽  
Negatively Associated ◽  
Crude Mortality Rate ◽  
Crude Mortality ◽  
Health Related ◽  
The Impact ◽  
The Relationship

BACKGROUND The impact of the COVID-19 pandemic has varied widely across nations and even in different regions of the same nation. Some of this variability may be due to the interplay of pre-existing demographic, socioeconomic, and health-related factors in a given population. OBJECTIVE The aim of this study was to examine the statistical associations between the statewise prevalence, mortality rate, and case fatality rate of COVID-19 in 24 regions in India (23 states and Delhi), as well as key demographic, socioeconomic, and health-related indices. METHODS Data on disease prevalence, crude mortality, and case fatality were obtained from statistics provided by the Government of India for 24 regions, as of June 30, 2020. The relationship between these parameters and the demographic, socioeconomic, and health-related indices of the regions under study was examined using both bivariate and multivariate analyses. RESULTS COVID-19 prevalence was negatively associated with male-to-female sex ratio (defined as the number of females per 1000 male population) and positively associated with the presence of an international airport in a particular state. The crude mortality rate for COVID-19 was negatively associated with sex ratio and the statewise burden of diarrheal disease, and positively associated with the statewise burden of ischemic heart disease. Multivariate analyses demonstrated that the COVID-19 crude mortality rate was significantly and negatively associated with sex ratio. CONCLUSIONS These results suggest that the transmission and impact of COVID-19 in a given population may be influenced by a number of variables, with demographic factors showing the most consistent association.

An Exploratory Analysis and Predictive SIR Model for the Early Onset of COVID-19 in Tamil Nadu, India

2021 ◽  
pp. 12-33
Author(s):  
Chandan Tanvi Mandapati
Keyword(s):  
Logistic Regression ◽  
Mortality Rate ◽  
Predictive Models ◽  
Tamil Nadu ◽  
Descriptive Analysis ◽  
Sir Model ◽  
Fatality Rate ◽  
Southern State ◽  
Crude Mortality Rate ◽  
Crude Mortality

The growth of COVID-19 (SARS-CoV-2) in India has been rampant. Despite having a relatively small value of R0, the spread of disease increases exponentially every consecutive day. This chapter aims to analyze and conduct a concise study for the southern state of Tamil Nadu in India and build non-linear predictive models that evaluate the transmission of coronavirus amongst locals. A logistic regression and SIR model are deployed to understand the potential spread of disease. Through descriptive analysis on theoretical segmented portions, districts in Tamil Nadu with a higher number of confirmed cases are identified. Computation of crude mortality rate, infection fatality rate, predictive models, illustrations, and their results are discussed analytically.

scholarly journals The Relationship Between Demographic, Socioeconomic, and Health-Related Parameters and the Impact of COVID-19 on 24 Regions in India: Exploratory Cross-Sectional Study

10.2196/23083 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e23083
Author(s):  
Ravi Philip Rajkumar
Keyword(s):  
Mortality Rate ◽  
Sex Ratio ◽  
Multivariate Analyses ◽  
Case Fatality ◽  
Negatively Associated ◽  
Crude Mortality Rate ◽  
Crude Mortality ◽  
Health Related ◽  
The Impact ◽  
The Relationship

Background The impact of the COVID-19 pandemic has varied widely across nations and even in different regions of the same nation. Some of this variability may be due to the interplay of pre-existing demographic, socioeconomic, and health-related factors in a given population. Objective The aim of this study was to examine the statistical associations between the statewise prevalence, mortality rate, and case fatality rate of COVID-19 in 24 regions in India (23 states and Delhi), as well as key demographic, socioeconomic, and health-related indices. Methods Data on disease prevalence, crude mortality, and case fatality were obtained from statistics provided by the Government of India for 24 regions, as of June 30, 2020. The relationship between these parameters and the demographic, socioeconomic, and health-related indices of the regions under study was examined using both bivariate and multivariate analyses. Results COVID-19 prevalence was negatively associated with male-to-female sex ratio (defined as the number of females per 1000 male population) and positively associated with the presence of an international airport in a particular state. The crude mortality rate for COVID-19 was negatively associated with sex ratio and the statewise burden of diarrheal disease, and positively associated with the statewise burden of ischemic heart disease. Multivariate analyses demonstrated that the COVID-19 crude mortality rate was significantly and negatively associated with sex ratio. Conclusions These results suggest that the transmission and impact of COVID-19 in a given population may be influenced by a number of variables, with demographic factors showing the most consistent association.

scholarly journals Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Joshua J. Levy ◽  
Rebecca M. Lebeaux ◽  
Anne G. Hoen ◽  
Brock C. Christensen ◽  
Louis J. Vaickus ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
Mortality Rate ◽  
Satellite Images ◽  
The United States ◽  
Mortality Rates ◽  
Image Features ◽  
County Level ◽  
Crude Mortality Rate ◽  
Crude Mortality

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking.Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age.Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

scholarly journals Analysis of death causes of residents in poverty-stricken Areas in 2020: take Liangshan Yi Autonomous Prefecture in China as an example

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Rujun Liao ◽  
Lin Hu ◽  
Qiang Liao ◽  
Tianyu Zhu ◽  
Haiqun Yang ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Life Expectancy ◽  
Malignant Tumors ◽  
Heart Diseases ◽  
Health Policies ◽  
Years Of Life Lost ◽  
Crude Mortality Rate ◽  
Crude Mortality ◽  
Standardized Mortality Rate ◽  
Death Causes

Abstract Background Continuous surveillance of death can measure health status of the population, reflect social development of a region, thus promote health service development in the region and improve the health level of local residents. Liangshan Yi Autonomous Prefecture was a poverty-stricken region in Sichuan province, China. While at the end of 2020, as the announcement of its last seven former severely impoverished counties had shaken off poverty, Liangshan declared victory against poverty. Since it is well known that the mortality and cause of death structure will undergo some undesirable changes as the economy develops, this study aimed to reveal the distribution of deaths, as well as analyze the latest mortality and death causes distribution characteristics in Liangshan in 2020, so as to provide references for the decision-making on health policies and the distribution of health resources in global poverty-stricken areas. Methods Liangshan carried out the investigation on underreporting deaths among population in its 11 counties in 2018, and combined with the partially available data from underreporting deaths investigation data in 2020 and the field experience, we have estimated the underreporting rates of death in 2020 using capture-recapture (CRC) method. The crude mortality rate, age-standardized mortality rate, proportion and rank of the death causes, potential years of life lost (PYLL), average years of life lost (AYLL), potential years of life lost rate (PYLLR), standardized potential years of life lost (SPYLL), premature mortality from non-communicable diseases (premature NCD mortality), life expectancy and cause-eliminated life expectancy were estimated and corrected. Results In 2020, Liangshan reported a total of 16,850 deaths, with a crude mortality rate of 608.75/100,000 and an age-standardized mortality rate of 633.50/100,000. Male mortality was higher than female mortality, while 0-year-old mortality of men was lower than women’s. The former severely impoverished counties’ age-standardized mortality and 0-year-old mortality were higher than those of the non-impoverished counties. The main cause of death spectrum was noncommunicable diseases (NCDs), and the premature NCD mortality of four major NCDs were 14.26% for the overall population, 19.16% for men and 9.27% for women. In the overall population, the top five death causes were heart diseases (112.07/100,000), respiratory diseases (105.85/100,000), cerebrovascular diseases (87.03/100,000), malignant tumors (73.92/100,000) and injury (43.89/100,000). Injury (64,216.78 person years), malignant tumors (41,478.33 person years) and heart diseases (29,647.83 person years) had the greatest burden on residents in Liangshan, and at the same time, the burden of most death causes on men were greater than those on women. The life expectancy was 76.25 years for overall population, 72.92 years for men and 80.17 years for women, respectively, all higher than the global level (73.3, 70.8 and 75.9 years). Conclusions Taking Liangshan in China as an example, this study analyzed the latest death situation in poverty-stricken areas, and proposed suggestions on the formulation of health policies in other poverty-stricken areas both at home and abroad.

scholarly journals Risk factors for incidence and case-fatality rates of healthcare-associated infections: a 20-year follow-up of a hospital-based cohort

2015 ◽  
Vol 144 (1) ◽  
pp. 198-206 ◽  
Author(s):  
R.-F. WANG ◽  
S.-H. SHEN ◽  
A. M.-F. YEN ◽  
T.-L. WANG ◽  
T.-N. JANG ◽  
...  
Keyword(s):  
Risk Factors ◽  
Mortality Rate ◽  
Incidence Rate ◽  
Case Fatality Rate ◽  
Tertiary Care ◽  
Case Fatality ◽  
Healthcare Associated Infections ◽  
Fatality Rate ◽  
Infection Site ◽  
Healthcare Associated

SUMMARYInformation is lacking on the integrated evaluation of mortality rates in healthcare-associated infections (HAIs). Our aim was to differentiate the risk factors responsible for the incidence from those for the case-fatality rates in association with HAIs. We therefore examined the time trends of both incidence and case-fatality rates over a 20-year period at a tertiary-care teaching medical centre in Taiwan and the mortality rate was expressed as the product of the incidence rate and the case-fatality rate. During the study period the overall mortality rate fell from 0·46 to 0·32 deaths/1000 patient-days and the incidence rate fell from 3·41 to 2·31/1000 patient-days, but the case-fatality rate increased marginally from 13·5% to 14·0%. The independent risk factors associated with incidence of HAIs were age, gender, infection site, admission type, and department of hospitalization. Significant prognostic factors for HAI case-fatality were age, infection site, intensive care, and clinical department. We conclude that the decreasing trend for the HAI mortality rate was accompanied by a significant decline in the incidence rate and this was offset by a slightly increasing trend in the case-fatality rate. This deconstruction approach could provide further insights into the underlying complex causes of mortality for HAIs.

The descriptive epidemiology of suicide in Tuscany, 1988-2002

2006 ◽  
Vol 15 (3) ◽  
pp. 202-210 ◽  
Author(s):  
Michele Arcangelo Martiello ◽  
Francesco Cipriani ◽  
Fabio Voller ◽  
Eva Buiatti ◽  
Mariano Giacchi
Keyword(s):  
Spatial Analysis ◽  
Rural Communities ◽  
Mortality Rate ◽  
Empirical Bayes ◽  
Mining District ◽  
Mine Closure ◽  
Descriptive Epidemiology ◽  
Bayes Estimates ◽  
Crude Mortality Rate ◽  
Crude Mortality

SUMMARYAims – To describe the epidemiology of Suicide in Tuscany according to the triad of time, place and person. Methods – The 4, 764 cases of suicide, defined according to categories E950-E959 of ICD-9 in Tuscany over the period 1988–2002, were obtained from the Tuscan Mortality Register. Mortality indicators were calculated and analyzed. The spatial analysis was carried out by deriving Empirical Bayes Estimates for the 287 municipalities. Results – The crude mortality rate in the 2000–2002 is 7.8 per 100000 population (male: 12.4; female: 3.5). The age-standardized rate in the 2000–2002 is 5.8 per 100, 000 population (male: 9.6; female: 2.6). The highest risk for suicide, especially in the case of males, are concentrated in the southern hinterland Tuscany, in a cluster of rural municipalities that represent the old mining district of Tuscany. The SMRs according to residential municipality (population per square kilometre), confirm a greater risk of suicide for males residing in rural communities. Conclusions – The cluster of excessive mortality from suicide in Southern Tuscany could be the consequence of social determinants, related to the urban and social crisis following agriculture decline and mine closure.Declaration of Interest: none.

scholarly journals Mortality among Guarani Indians in Southeastern and Southern Brazil

2011 ◽  
Vol 27 (suppl 2) ◽  
pp. s222-s236 ◽  
Author(s):  
Andrey Moreira Cardoso ◽  
Carlos E. A. Coimbra Jr. ◽  
Carla Tatiana Garcia Barreto ◽  
Guilherme Loureiro Werneck ◽  
Ricardo Ventura Santos
Keyword(s):  
Mortality Rate ◽  
Health Inequalities ◽  
Causes Of Death ◽  
Infant Mortality Rate ◽  
Indigenous Populations ◽  
Southeastern Brazil ◽  
Infant Deaths ◽  
Life Expectancy At Birth ◽  
Crude Mortality Rate ◽  
Crude Mortality

Worldwide, indigenous peoples display a high burden of disease, expressed by profound health inequalities in comparison to non-indigenous populations. This study describes mortality patterns among the Guarani in Southern and Southeastern Brazil, with a focus on health inequalities. The Guarani population structure is indicative of high birth and death rates, low median age and low life expectancy at birth. The crude mortality rate (crude MR = 5.0/1,000) was similar to the Brazilian national rate, but the under-five MR (44.5/1,000) and the infant mortality rate (29.6/1,000) were twice the corresponding MR in the South and Southeast of Brazil. The proportion of post-neonatal infant deaths was 83.3%, 2.4 times higher than general population. The proportions of ill-defined (15.8%) and preventable causes (51.6%) were high. The principal causes of death were respiratory (40.6%) and infectious and parasitic diseases (18.8%), suggesting precarious living conditions and deficient health services. There is a need for greater investment in primary care and interventions in social determinants of health in order to reduce the health inequalities.

scholarly journals Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Neighborhood Features

2020 ◽  
Author(s):  
Joshua J. Levy ◽  
Rebecca M. Lebeaux ◽  
Anne G. Hoen ◽  
Brock C. Christensen ◽  
Louis J. Vaickus ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
Mortality Rate ◽  
Satellite Images ◽  
The United States ◽  
Mortality Rates ◽  
Image Features ◽  
County Level ◽  
Crude Mortality Rate ◽  
Crude Mortality

AbstractWhat is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?BackgroundFollowing a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.MethodsSatellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.ResultsPredicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r=0.72). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g. sidewalks, driveways and hiking trails) associated with lower mortality.ConclusionsThe application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

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